(Copyright: Thinkstock). Our eyes are remarkable in making almost instant sense of the world around us in ways that even the most sophisticated machines can’t do. So what cues do we pick up that make sure we can see the wood for the trees?

The BBC provides this interesting view on the relationship between sight and other forms of sense:

One of the unforeseen boons of research on artificial intelligence is that it has revealed much about our own intelligence. Some aspects of human perception and thought can be mimicked easily, indeed vastly surpassed, by machines, while others are extremely hard to reproduce. Take visual processing. We can give a satellite an artificial eye that can photograph your backyard from space, but making machines that can interpret what they “see” is still very challenging.

That realisation should make us appreciate our own virtuosity in making almost instant sense of the world around us – even scenes as complex as woods or forests crammed with trees, some overlapping, occluded, moving, or viewed at odd angles or in poor light. This ability to deconstruct immense visual complexity is usually regarded as an exquisite refinement of the neural circuitry of the human brain: in other words, it’s all in the head. It’s seldom asked what are the rules governing the visual stimulus in the first place; that is, what are the patterns that allow us to see the wood and the trees.

But a paper published in the journal Physical Review Letters stands the problem of image analysis on its head by asking not how we solve the problem of interpreting the world but what sort of problem it is in the first place. What hidden patterns exist in the visual stimulus?

Answering that question involves a remarkable confluence of scientific concepts. There is a growing awareness that how data is encoded, inter-converted and transported – whether in computers, genes or the quantum states of atoms – is closely linked to the field of thermodynamics, which was originally devised to understand how heat flows in engines and other machinery. For example, any processing of information – changing a bit in a computer’s binary memory from a 1 to a 0, say – generates heat.

A team at Princeton University led by William Bialek now integrates these ideas with concepts from image processing and neuroscience. The consequences are striking. Bialek and his colleagues Greg Stephens, Thierry Mora and Gasper Tkacik find that in a pixellated monochrome image of the woods in Hacklebarney State Park, New Jersey, some groups of black and white pixels are more common than other, seemingly similar ones. And they argue that such images can be assigned a kind of “temperature”, which reflects the way the black and white pixels are distributed across the visual field…

Read the rest of the story here.